changes for inpainting for #35

support for --medvram
attempt to support share

1 files changed, 10 insertions, 9 deletions

diff --git a/README.md b/README.md
index 4a393c00..7458f1ae 100644
--- a/README.md
+++ b/README.md
@@ -71,10 +71,10 @@ Run the command to start web ui:
 python stable-diffusion-webui/webui.py

 ```

 

-If you have a 4GB video card, run the command with `--lowvram` argument:

+If you have a 4GB video card, run the command with either `--lowvram` or `--medvram` argument:

 

 ```

-python stable-diffusion-webui/webui.py --lowvram

+python stable-diffusion-webui/webui.py --medvram

 ```

 

 After a while, you will get a message like this:

@@ -280,17 +280,18 @@ print("Seed was: " + str(processed.seed))
 display(processed.images, processed.seed, processed.info)

 ```

 

-### `--lowvram`

+### 4GB videocard support

 Optimizations for GPUs with low VRAM. This should make it possible to generate 512x512 images on videocards with 4GB memory.

 

-The original idea of those optimizations is by basujindal: https://github.com/basujindal/stable-diffusion. Model is separated into modules,

-and only one module is kept in GPU memory; when another module needs to run, the previous is removed from GPU memory.

-

-It should be obvious but the nature of those optimizations makes the processing run slower -- about 10 times slower

+`--lowvram` is a reimplementation of optimization idea from by [basujindal](https://github.com/basujindal/stable-diffusion).

+Model is separated into modules, and only one module is kept in GPU memory; when another module needs to run, the previous

+is removed from GPU memory. The nature of this optimization makes the processing run slower -- about 10 times slower

 compared to normal operation on my RTX 3090.

 

-This is an independent implementation that does not require any modification to original Stable Diffusion code, and

-with all code concenrated in one place rather than scattered around the program.

+`--medvram` is another optimization that should reduce VRAM usage significantly by not peocessing conditional and

+unconditional denoising in a same batch.

+

+This implementation of optimization does not require any modification to original Stable Diffusion code.

 

 ### Inpainting

 In img2img tab, draw a mask over a part of image, and that part will be in-painted.